CN113361058A

CN113361058A - Method and device for determining a wind parameter representative of a wind farm

Info

Publication number: CN113361058A
Application number: CN202010146275.1A
Authority: CN
Inventors: 刘虎
Original assignee: Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
Current assignee: Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
Priority date: 2020-03-05
Filing date: 2020-03-05
Publication date: 2021-09-07
Anticipated expiration: 2040-03-05
Also published as: CN113361058B

Abstract

A method and a device for determining a representative wind parameter of a wind park are provided, the method comprising: acquiring wind parameters of the plurality of machine positions; respectively inputting the acquired wind parameters of the plurality of machine positions into corresponding load prediction models to obtain the key loads of the plurality of machine positions; determining load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions; grouping the plurality of machine sites based on the determined load attribute indexes to obtain a plurality of fan sets; performing the following for any of the plurality of fan sets: determining an envelope wind parameter of any fan set, and determining a representative wind parameter of any fan set based on the envelope wind parameter of any fan set. By adopting the method and the equipment, the accuracy of the representative wind parameter of the wind power plant can be ensured, and the economic benefit of wind power plant design is effectively improved.

Description

Method and device for determining a wind parameter representative of a wind farm

Technical Field

The present invention relates generally to the field of wind power generation, and more particularly to a method and apparatus for determining a representative wind parameter for a wind farm.

Background

In the process of selecting the type of the wind generating set in the wind power plant project, the structural safety of the wind generating set arranged in the wind power plant is checked based on the wind parameter of the wind power plant, and the method is an important link of the wind power plant design.

At present, with the increasingly complex design of the terrain of a wind farm and the design of wind generating sets, in order to meet the accuracy of structural safety checking, wind parameters should be selected respectively for machine sites where the wind generating sets needing structural safety checking are located, however, as the structural safety checking needs to spend a lot of resources and time, in practical application, the representative wind parameters of the wind farm are generally obtained firstly and used as the wind parameters of each wind generating set for structural safety checking, and then the representative wind parameters of the wind farm are used for performing structural safety checking on each wind generating set.

The representative wind parameters of a wind farm are generally obtained in the prior art by the following three ways: the first method is to combine the worst wind parameters of each machine position point for arranging the wind generating sets in the wind power plant into a group of wind parameters as representative wind parameters, and perform structural safety check on all the wind generating sets. And secondly, grouping the wind parameters of each machine position point according to the traditional experience, then selecting envelope wind parameters, and taking the envelope wind parameters as representative wind parameters to perform structural safety check on all wind generating sets. And the third method is to select representative wind parameters directly through a machine locus wind parameter clustering analysis model.

However, the above methods cannot obtain accurate representative wind parameters due to their respective defects, for example, the first method often excessively increases the severity of the wind parameters of the site where the wind farm is located, so that the evaluation of the structural safety check of the wind turbine generator system is excessively conservative, and the economy of the type selection of the wind turbine generator system is poor. The second approach, while alleviating the above drawbacks to some extent, does not form a safety quantification criterion based on empirically selected wind parameters, and such grouping often requires manual trial and iteration. The third method is more clustering analysis considered from wind parameters, the influence attribute of each wind parameter on the safety of the wind generating set cannot be well reflected and quantified, and the influence on the structure of the wind generating set and the load of the wind generating set after the wind parameter coupling is also lack of consideration.

Disclosure of Invention

An exemplary embodiment of the present invention is to provide a method and a device for determining a representative wind parameter of a wind farm, which can overcome the defect of inaccurate acquisition of the representative wind parameter of the wind farm in the prior art.

According to an aspect of an exemplary embodiment of the present invention, there is provided a method of determining a representative wind parameter of a wind park comprising a plurality of machine sites for arranging wind turbine generator sets, characterized in that the method comprises: acquiring wind parameters of the plurality of machine positions; respectively inputting the acquired wind parameters of the plurality of machine positions into corresponding load prediction models to obtain the key loads of the plurality of machine positions; determining load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions; grouping the plurality of machine sites based on the determined load attribute indexes to obtain a plurality of fan sets; performing the following for any of the plurality of fan sets: determining an envelope wind parameter of any fan set, and determining a representative wind parameter of any fan set based on the envelope wind parameter of any fan set.

Optionally, the step of determining load attribute indicators of the wind parameters of the plurality of machine sites based on the obtained key loads of the plurality of machine sites comprises: and respectively calculating the ratio of the key load of each machine position point in the plurality of machine position points to the maximum value of the key loads of the plurality of machine position points, and determining each calculated ratio as the load attribute index of the wind parameter of each corresponding machine position point.

Optionally, grouping the plurality of machine sites based on the determined load attribute index, and obtaining a plurality of fan sets includes: respectively determining the absolute value of the deviation of each load attribute index from the maximum value of all the determined load attribute indexes; and determining a numerical range to which the absolute value of each deviation belongs, wherein one numerical range corresponds to one fan set so as to obtain the plurality of fan sets.

Optionally, the step of determining a representative wind parameter of any one set of fans based on the envelope wind parameter of any one set of fans comprises: inputting the enveloping wind parameters of any fan set into a load prediction model corresponding to a preset machine position point to obtain an enveloping key load of the preset machine position point; determining the ratio of the envelope key load to the envelope key load as an envelope load attribute index corresponding to an envelope wind parameter of any fan set; respectively calculating the ratio of the key load of each machine position point in any fan set to the envelope key load, and determining each calculated ratio as a new load attribute index of the wind parameter of each corresponding machine position point; and determining the representative wind parameter of any fan set according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine position point in any fan set and the load attribute index of the wind parameter of each machine position point in any fan set.

Optionally, the step of determining a representative wind parameter of any one of the fan sets according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine site in any one of the fan sets, and the load attribute index of the wind parameter of each machine site in any one of the fan sets includes: comparing the envelope load attribute index with a maximum value in new load attribute indexes of wind parameters of machine sites in any fan set, if the absolute value of the deviation between the envelope load attribute index and the maximum value is not larger than a first threshold value, determining the envelope wind parameter corresponding to the envelope load attribute index as a representative wind parameter of any fan set, if the absolute value of the deviation between the envelope load attribute index and the maximum value is larger than the first threshold value, comparing the maximum value with the minimum value in the load attribute indexes of the wind parameters of the machine sites in any fan set, if the absolute value of the deviation between the maximum value and the minimum value in the load attribute indexes is larger than a second threshold value, continuing to group the machine sites in any fan set until the envelope wind parameters of a group can be used as the representative wind parameters of the group, wherein the second threshold is less than the first threshold.

Optionally, the critical load for any of each of the loci is obtained by: inputting the wind parameters of any machine site into a load prediction model corresponding to the machine type of the wind generating set arranged at any machine site to obtain the key load of any machine site, wherein the load prediction model corresponding to the wind generating set of any machine type is constructed in the following way: acquiring a plurality of groups of sample wind parameters, wherein the plurality of groups of sample wind parameters are acquired based on a preset sample design method; respectively determining the key load of the wind generating set of any model under each group of sample wind parameters; and constructing a load prediction model corresponding to the wind generating set of any model by using the multiple groups of sample wind parameters and the determined key loads.

Optionally, the multiple groups of sample wind parameters include sample wind parameters under multiple sub-operating condition groups, and the critical load includes a limit load variable that is most affected by the sample wind parameters in the limit load, where the step of determining the critical load of the wind turbine generator set of any model under any group of sample wind parameters includes: respectively determining the limit load of the wind generating set of any model under the sample wind parameter of each sub-working condition group through a simulation program, and determining the maximum value of all the determined limit loads as the limit load variable of the wind generating set of any model, wherein the load prediction model comprises a model for determining the limit load variable of the wind generating set of any model.

Optionally, the multiple groups of sample wind parameters include sample wind parameters under multiple sub-working conditions, and the critical load includes a fatigue load variable that is most affected by the sample wind parameters in the fatigue loads, wherein the step of determining the critical load of the wind turbine generator set of any model under any group of sample wind parameters includes: respectively determining fatigue loads of the wind generating set of any model under the sample wind parameters of each sub-working condition through a simulation program, carrying out weighting processing on all the determined fatigue loads to obtain a load weighted value, and determining the obtained load weighted value as a fatigue load variable of the wind generating set of any model, wherein the load prediction model comprises a model for determining the fatigue load variable of the wind generating set of any model.

Optionally, the step of constructing a load prediction model corresponding to the wind generating set of any model by using the plurality of sets of sample wind parameters and the determined critical loads includes: and performing multiple linear regression processing on the multiple groups of sample wind parameters and the determined key loads to obtain a load prediction model corresponding to the wind generating set of any model.

Optionally, the critical load of any one machine site refers to a load variable of a wind generating set arranged at the any one machine site, which is most affected by the sample wind parameter.

Optionally, determining the critical load of any one of the machine sites by: determining a plurality of load variables of each set of sample wind parameters of a wind generating set for testing, which is arranged at the same wind generating set model as the wind generating set arranged at any machine site, in the plurality of sets of sample wind parameters; and determining the load variable with the largest change amplitude in the plurality of determined load variables as the key load of any machine point.

According to another aspect of an exemplary embodiment of the present invention, there is provided a device for determining a representative wind parameter of a wind park comprising a plurality of machine sites for arranging wind energy installations, characterized in that the device comprises: the wind parameter acquisition unit is used for acquiring wind parameters of the plurality of machine sites; the key load acquisition unit is used for respectively inputting the acquired wind parameters of the plurality of machine points into corresponding load prediction models to obtain the key loads of the plurality of machine points; a load attribute index determination unit which determines load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions; the fan set determining unit is used for grouping the plurality of machine positions based on the determined load attribute indexes to obtain a plurality of fan sets; a representative wind parameter determination unit that performs, for any one of the plurality of fan sets: determining an envelope wind parameter of any fan set, and determining a representative wind parameter of any fan set based on the envelope wind parameter of any fan set.

Optionally, the load attribute index determining unit calculates a ratio of a key load of each of the plurality of machine points to a maximum value of the key loads of the plurality of machine points, and determines each calculated ratio as the load attribute index of the wind parameter of each corresponding machine point.

Optionally, the fan set determining unit determines an absolute value of a deviation between each load attribute index and a maximum value of all the determined load attribute indexes, and determines a numerical range to which the absolute value of each deviation belongs, where one numerical range corresponds to one fan set, so as to obtain the plurality of fan sets.

Optionally, the representative wind parameter determination unit includes: the envelope key load obtaining unit is used for inputting the envelope wind parameters of any fan set into a load prediction model corresponding to a preset machine position point to obtain the envelope key load of the preset machine position point; the envelope load attribute index determining unit is used for determining the ratio of the envelope key load to the envelope key load as an envelope load attribute index corresponding to an envelope wind parameter of any fan set; the new load attribute index determining unit is used for respectively calculating the ratio of the key load of each machine position point in any fan set to the envelope key load and determining each calculated ratio as the new load attribute index of the wind parameter of each corresponding machine position point; and the fan set representative wind parameter determining unit is used for determining the representative wind parameter of any fan set according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine position point in any fan set and the load attribute index of the wind parameter of each machine position point in any fan set.

Optionally, the wind turbine set representative wind parameter determining unit compares the envelope load attribute index with a maximum value of new load attribute indexes of wind parameters of each machine location point in any wind turbine set, if an absolute value of a deviation between the envelope load attribute index and the maximum value is not greater than a first threshold, determines the envelope wind parameter corresponding to the envelope load attribute index as the representative wind parameter of any wind turbine set, if the absolute value of a deviation between the envelope load attribute index and the maximum value is greater than the first threshold, compares the maximum value with a minimum value of the load attribute indexes of the wind parameters of each machine location point in any wind turbine set, and if the absolute value of a deviation between the maximum value and the minimum value in the load attribute indexes is greater than a second threshold, continues to group the machine location points in any wind turbine set, the envelope wind parameter up to a group can be a representative wind parameter of the group, wherein the second threshold value is smaller than the first threshold value.

Optionally, the critical load obtaining unit inputs the wind parameter of the any one of the machine sites to a load prediction model corresponding to a model of a wind turbine generator set arranged at the any one of the machine sites to obtain the critical load of the any one of the machine sites, wherein the apparatus further includes: the system comprises a model construction unit, wherein the model construction unit acquires a plurality of groups of sample wind parameters, the plurality of groups of sample wind parameters are acquired based on a preset sample design method, the key loads of the wind generating set of any model under each group of sample wind parameters are respectively determined, and a load prediction model corresponding to the wind generating set of any model is constructed by using the plurality of groups of sample wind parameters and the determined key loads.

Optionally, the multiple groups of sample wind parameters include sample wind parameters under multiple sub-operating condition groups, and the critical load includes a limit load variable that is most affected by the sample wind parameters in the limit load, where the model building unit determines, through a simulation program, the limit load of the wind turbine generator set of any one of the models under the sample wind parameters of each of the sub-operating condition groups, and determines a maximum value of all the determined limit loads as the limit load variable of the wind turbine generator set of any one of the models, where the load prediction model includes a model for determining the limit load variable of the wind turbine generator set of any one of the models.

Optionally, the multiple groups of sample wind parameters include sample wind parameters under multiple sub-working conditions, the key load includes a fatigue load variable that is most affected by the sample wind parameters in fatigue loads, the model construction unit determines the fatigue loads of the wind turbine generator set of any model under each sub-working condition through a simulation program, performs weighting processing on all the determined fatigue loads to obtain a load weighted value, and determines the obtained load weighted value as the fatigue load variable of the wind turbine generator set of any model, where the load prediction model includes a model for determining the fatigue load variable of the wind turbine generator set of any model.

Optionally, the model construction unit performs multiple linear regression processing on the multiple groups of sample wind parameters and the determined key loads to obtain a load prediction model corresponding to the wind generating set of any model.

Optionally, the model building unit determines the critical load of any of the machine sites by: determining a plurality of load variables of the wind generating set for testing, which is the same as the wind generating set model arranged at any machine site, under each set of sample wind parameters in the plurality of sets of sample wind parameters, and determining the load variable with the largest change amplitude in the plurality of determined load variables as the key load of any machine site.

On the other hand, an embodiment of the present invention further provides an electronic device, where the electronic device includes: a processor, a memory, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, determines a representative wind parameter for a wind farm.

In another aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method for determining a representative wind parameter of a wind farm.

The method and the equipment for determining the representative wind parameters of the wind power plant can solve the problems that influence on the wind parameters is not considered comprehensively and the relation between the wind parameters and the load is not utilized sufficiently in the method for determining the representative wind parameters in the prior art, and the like.

Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.

Drawings

The above and other objects of exemplary embodiments of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings which illustrate exemplary embodiments, wherein:

FIG. 1 shows a flow chart of a method of determining a representative wind parameter of a wind farm according to an exemplary embodiment of the present invention;

FIG. 2 illustrates a schematic diagram of a load attribute indicator for a wind parameter for each machine site in accordance with an exemplary embodiment of the present invention;

FIG. 3 illustrates a schematic diagram of a load property indicator for a wind parameter for each machine site according to another exemplary embodiment of the present invention;

FIG. 4 illustrates a schematic diagram of fatigue load attribute indicators for wind parameters for various sites included in the first set of wind turbines according to an exemplary embodiment of the present invention;

FIG. 5 illustrates a schematic diagram of fatigue load attribute indicators for wind parameters for various sites included in the second set of wind turbines according to an exemplary embodiment of the present invention;

FIG. 6 illustrates an example of the ordering of the envelope load attribute index and the new load attribute index for each machine site in accordance with an exemplary embodiment of the present invention;

FIG. 7 illustrates an example of the ranking results of the envelope load attribute index and the new load attribute index for each machine site according to another exemplary embodiment of the present invention;

FIG. 8 illustrates an example of the ranking results of the envelope load attribute index and the new load attribute index for each machine site according to another exemplary embodiment of the present invention;

fig. 9 shows a block diagram of an apparatus for determining a representative wind parameter of a wind farm according to an exemplary embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below in order to explain the present invention by referring to the figures.

Fig. 1 shows a flow chart of a method of determining a representative wind parameter of a wind farm according to an exemplary embodiment of the invention. Here, the wind park comprises a plurality of machine sites for arranging wind generating sets.

As shown in fig. 1, in step S100, wind parameters of a plurality of airport points are acquired. As an example, the wind parameters may include, but are not limited to, at least one of: turbulence intensity, air density, inflow angle, wind shear, wind frequency distribution value.

In step S200, the acquired wind parameters of the plurality of machine positions are respectively input to the corresponding load prediction models, so as to acquire the critical loads of the plurality of machine positions. Here, the critical load of any machine site refers to a load variable of a wind turbine generator set arranged at the any machine site, which is most affected by wind parameters.

In one example, wind parameters for any one of the machine sites may be input to a load prediction model corresponding to a model of a wind turbine generator set disposed at any one of the machine sites to obtain a critical load for any one of the machine sites.

Specifically, the load prediction model corresponding to the wind generating set of any model can be constructed in the following way:

first, a plurality of sets of sample wind parameters are acquired, wherein the plurality of sets of sample wind parameters are acquired based on a preset sample design method including, as an example, an orthogonal method and a uniform design method. Here, the Orthogonal method (i.e., Orthogonal experimental design) is a design method for studying multi-factor multilevel, and the Orthogonal method is to select some representative points (as an example, the representative points may refer to the above-mentioned turbulence intensity, air density, inflow angle and/or wind shear) from a full test according to orthogonality, and the representative points have the characteristics of being "uniformly dispersed and uniformly comparable". For example, the turbulence intensity, the air density, the inflow angle and the wind shear of a plurality of other working condition groups or working conditions can be selected by taking the values of the turbulence intensity, the air density, the inflow angle and the wind shear in the reference working condition group/reference working condition as references and taking the principle of 'uniform dispersion and regularity comparability', and the values of the parameters are combined to obtain a plurality of groups of sample wind parameters. In addition, the homogeneous design method is a test method in which a test is arranged using a homogeneous design table and data analysis is performed using regression analysis, and the basic idea is to make test points have a good uniform dispersion in a factor space.

And then, respectively determining the key loads of the wind generating set of any model under each group of sample wind parameters, and constructing a load prediction model corresponding to the wind generating set of any model by using the multiple groups of sample wind parameters and the determined key loads.

In one example, the plurality of sets of sample wind parameters may include sample wind parameters under a plurality of sub-operating condition sets, and the critical load may include a limit load variable of the limit load that is most affected by the sample wind parameters. In this case, the step of determining the critical load of any model of wind generating set under each set of sample wind parameters may determine the critical load of any model of wind generating set under any set of sample wind parameters by: and respectively determining the limit load of the wind generating set of any model under the sample wind parameter of each sub-working condition group through a simulation program, and determining the maximum value of all the determined limit loads as the limit load variable of the wind generating set of any model, wherein the load prediction model can comprise a model for determining the limit load variable of the wind generating set of any model.

In the following, a process of determining a critical load of a wind turbine generator set of any model under any set of sample wind parameters will be described in detail with reference to a specific example, where the multiple sets of sample wind parameters include sample wind parameters under multiple sub-operating condition sets. Table 1 shows the correspondence between each sub-operating condition group of a wind turbine generator set of any model and a sample wind parameter.

TABLE 1

In the example shown in table 1, the first column is the number of the sub-condition groups, and the wind parameter that is hooked in each row is the sample wind parameter under the sub-condition group corresponding to the row, where ETM3 refers to the turbulence intensity that is selected at intervals of 3m/s between the cut-in wind speed and the cut-out wind speed, and so on. Ir-2 refers to the conventional turbulence intensity corresponding to a wind speed section 2m/s lower than the rated wind speed of the wind generating set, Ir +2 refers to the conventional turbulence intensity corresponding to a wind speed section 2m/s higher than the rated wind speed of the wind generating set, and so on. I16 refers to the conventional turbulence intensity at the factory maintenance wind speed of 16 m/s. Iout refers to the turbulence intensity corresponding to a preset wind speed section with a wind speed representative value of cut-out wind speed. It should be understood that table 1 shows only a partial sub-regime group and the wind parameters for the partial sub-regime group.

Specifically, in this example, where sub-operating condition groups and wind parameters corresponding to each sub-operating condition group are determined, various simulation programs may be utilized to obtain the ultimate load of any model of wind generating set at the wind parameters for each sub-operating condition group. As an example, the Bladed simulation software may be utilized to obtain the ultimate load of any model of wind generating set under the wind parameters under each sub-operating condition group. The invention is not limited to the above, and the ultimate load of the wind generating set of any model under the wind parameter of each sub-working condition group can be obtained through simulation of other software (for example, Hawc2, Simpack, Fast, etc.).

Then, the maximum value of all the determined limit loads can be determined as the limit load variable (i.e. the critical load) of the wind generating set of any model.

In another example, the critical loads may include a fatigue load variable that is most affected by the sample wind parameters among the fatigue loads, in which case the step of determining the critical loads of any model of wind generating set at each set of sample wind parameters may determine the critical loads of any model of wind generating set at any set of sample wind parameters by: respectively determining fatigue loads of the wind generating set of any model under the sample wind parameters of each sub-working condition through a simulation program, carrying out weighting processing on all the determined fatigue loads to obtain a load weighted value, and determining the obtained load weighted value as a fatigue load variable of the wind generating set of any model, wherein the load prediction model comprises a model for determining the fatigue load variable of the wind generating set of any model.

In the following, a process of determining a critical load of a wind turbine generator set of any model under any group of sample wind parameters will be described in detail with reference to a specific example, where the multiple groups of sample wind parameters include sample wind parameters under multiple sub-conditions. And table 2 shows the corresponding relation between each sub-working condition of the wind generating set of any model and the sample wind parameter.

TABLE 2

In the example shown in table 2, the first column is the number of the sub-conditions, and the wind parameter hooked in each row is the wind parameter in the sub-condition corresponding to the row, where I5 refers to the turbulence intensity corresponding to the preset wind speed segment with the wind speed segment representative value of 5m/s, I7 refers to the turbulence intensity corresponding to the preset wind speed segment with the wind speed segment representative value of 7m/s, and so on. Iin denotes a turbulence intensity corresponding to a preset wind speed section in which a wind speed representative value is a cut-in wind speed, Ir denotes a turbulence intensity corresponding to a preset wind speed section in which a wind speed representative value is a rated wind speed, Iout denotes a turbulence intensity corresponding to a preset wind speed section in which a wind speed representative value is a cut-out wind speed, and Iend denotes a turbulence intensity corresponding to a preset wind speed section in which a wind speed representative value is a cut-off wind speed. It should be understood that table 3 shows only partial sub-conditions and wind parameters corresponding to the partial sub-conditions, where the cut-off wind speed refers to a wind speed parameter commonly used in load simulation, and is typically 0.7 times of the reference wind speed.

Specifically, in this example, with the sub-conditions and the sample wind parameters corresponding to each sub-condition determined, various simulation programs may be utilized to obtain the fatigue loads for any model of wind generating set at the sample wind parameters for each sub-condition. As an example, the fatigue loads of any model of wind generating set under the sample wind parameters of each sub-operating condition group can be obtained by using the Bladed simulation software. The invention is not limited to the above, and the fatigue load of any model of wind generating set under the sample wind parameter under each sub-condition can be obtained through simulation by other software (for example, Hawc2, Simpack, Fast, etc.).

Then, all determined fatigue loads can be weighted to obtain a load weighted value, and the obtained load weighted value is determined as a fatigue load variable (namely, a critical load) of the wind generating set of any model.

For example, the load weight value may be obtained using the following equation (1):

in the formula (1), F represents a load weight value, m represents a Wohler (stress-life) index of a fatigue load stress-cycle number curve, and F_iRepresenting the fatigue load p of a wind generating set of any model under the sample wind parameter under the ith sub-working condition_iThe occurrence frequency ratio of the ith sub-working condition is represented, i is larger than or equal to 1, n is the number of the sub-working conditions, a fatigue load stress-cycle frequency curve represents the relation between the fatigue strength and the fatigue life of a standard test piece under certain cycle characteristics, the ordinate is the fatigue strength of the standard test piece made of materials, the abscissa is a logarithmic value lgN of the fatigue life, and the occurrence frequency ratio of the ith sub-working condition is the ratio of the occurrence frequency of the ith sub-working condition in a preset time period to the occurrence frequency of all the sub-working conditions in the preset time period.

In addition, when the step of constructing the load prediction model corresponding to the wind generating set of any model by using the plurality of groups of sample wind parameters and the determined key loads is carried out, the load prediction model corresponding to the wind generating set of any model can be obtained by carrying out multiple linear regression processing on the plurality of groups of sample wind parameters and the determined key loads. In addition, the load prediction model can be trained by taking a plurality of groups of sample wind parameters and the determined key load as training samples, so that the load prediction model corresponding to the wind generating set of any model can be obtained. In addition, the load prediction model corresponding to the wind generating set of any model may also be obtained by using other existing manners with the multiple sets of sample wind parameters and the determined critical loads, which is not limited in any way by the embodiment of the present invention.

In addition, the obtained load prediction model corresponding to the wind generating set of any model can be verified in any existing manner, for example, whether the constructed load prediction model is reliable can be determined by judging whether the sample precision and the test precision are within the corresponding predetermined ranges. By the method, the reliability of the load prediction model is effectively improved.

On the other hand, in the example of the present invention, the critical load of any one machine site may refer to the load variable at which the wind turbine generator set arranged at any one machine site is most affected by the above-mentioned sample wind parameter. In this case, the critical load of any one of the machine sites (i.e., the load variable that is most affected by the sample wind parameter for a certain model of wind turbine generator set disposed at any one of the machine sites) can be determined by: and determining a plurality of load variables of the wind generating set for testing, which is the same as the wind generating set arranged at any machine site, under each set of sample wind parameters in the plurality of sets of sample wind parameters, and determining the load variable with the largest change amplitude in the determined plurality of load variables as the key load of any machine site (namely, the load variable of the wind generating set arranged at any machine site, which is influenced most by the sample wind parameters).

For example, assuming that 50 sets of sample wind parameters are acquired, first, a plurality of load variables of the wind turbine generator set for test under the 50 sets of sample wind parameters, which are the same as the model of the wind turbine generator set arranged at any site, are determined, where the load variables may include limit load variables of each component of the wind turbine generator set for test under any set of sample wind parameters or fatigue load variables of each component of the wind turbine generator set for test under any set of sample wind parameters.

For example, the load variable may be a limit load variable of a blade root of the wind turbine generator system for testing under any set of sample wind parameters, a limit load variable of a hub of the wind turbine generator system for testing under any set of sample wind parameters, a limit load variable of a yaw bearing of the wind turbine generator system for testing under any set of sample wind parameters, a limit load variable of a tower bottom of the wind turbine generator system for testing under any set of sample wind parameters, and the like, and the load variable may be a fatigue load variable of a blade root of the wind turbine generator system for testing under any set of sample wind parameters, a fatigue load variable of a fixed hub of the wind turbine generator system for testing under any set of sample wind parameters, a fatigue load variable of a yaw bearing of the wind turbine generator system for testing under any set of sample wind parameters, and the like, The fatigue load variables and the like of the tower bottom of the wind generating set for testing under any group of sample wind parameters are not listed in the invention. In an embodiment of the present invention, the tower bottom may refer to a tower bottom flange position.

Then, the load variable with the largest change amplitude in the various load variables of the wind generating set for testing under the 50 sets of sample wind parameters is determined. For example, for any load variable, the variation range of any load variable may be obtained by dividing the difference between the maximum value and the minimum value in any load variable under the 50 sets of sample wind parameters by the maximum value and then multiplying by one hundred percent, and accordingly, after obtaining the variation ranges of all load variables, the load variable with the largest variation range among the multiple load variables may be determined as the critical load of the machine site, that is, the load variable with the largest influence of the sample wind parameters on the wind turbine generator set for testing, that is, the critical load of the wind turbine generator set of any machine site. For example, the ultimate load variable of the wind generating set at any machine position, which is obtained in the above manner and is most affected by the sample wind parameter, is the tower bottom ultimate load, and the ultimate load variable of the wind generating set at any machine position, which is most affected by the sample wind parameter, is also the tower bottom ultimate load.

In addition, it should be noted that the number of groups of the sample wind parameters may also be other numbers obtained according to actual situations, and the embodiment of the present invention is not limited in any way here.

In step S300, load attribute indexes of the plurality of machine positions are determined based on the obtained key loads of the plurality of machine positions.

In one example, a ratio of a critical load of each of the plurality of machine sites to a maximum value of the critical loads of the plurality of machine sites may be calculated, and the calculated ratios are determined as a load attribute index of the wind parameter of the corresponding machine sites.

Fig. 2 shows a schematic diagram of load property indicators of wind parameters for respective machine sites arranged at a wind farm in case the critical loads comprise the extreme load variables of the extreme loads most affected by the wind parameters. Fig. 3 shows a schematic diagram of load property indicators of wind parameters of machine sites arranged at a wind farm in case that the critical loads include a fatigue load variable most affected by the wind parameters among the fatigue loads.

As shown in fig. 2 and 3, the abscissa in fig. 2 and 3 represents all machine sites included in the wind farm for arranging the wind turbine generators, and the ordinate represents the load property index associated with the wind parameter of each machine site, for example, the limit load property index in fig. 2 and the fatigue load property index in fig. 3. Here, it should be noted that the machine sites shown in fig. 2 or fig. 3 should in principle be all machine sites in the wind farm, but due to the limited space, in the example of the invention only the number of machine sites comprised in fig. 2 or fig. 3 is taken as the total number of machine sites comprised in the wind farm. In practice the total number may be increased or decreased depending on the actual arrangement. Furthermore, the load attribute indexes of the wind parameters of the machine sites may be sorted according to a preset sorting rule, for example, the preset sorting rule may be arranged in a descending order, where fig. 2 or fig. 3 shows the result after sorting according to the descending order.

In step S400, a plurality of machine locations are grouped based on the determined load attribute index, obtaining a plurality of fan sets.

As an example, an absolute value of a deviation of each load attribute index from a maximum value among all the determined load attribute indexes may be determined, and a numerical range to which the absolute value of each deviation belongs is determined, one numerical range corresponding to one fan set, so as to obtain a plurality of fan sets.

In an example of the present invention, the deviation may include any one of: the difference and the percentage of the difference. Further, the deviation may be other physical quantities set according to actual conditions, and the embodiment of the present invention is not limited in any way herein.

For example, in the case where the deviation includes a percentage of the difference, in the example shown in fig. 2, the maximum value of all the determined limit load attribute indexes is the limit load attribute index 1 corresponding to the machine position YA19, and the absolute value of the percentage of the difference between the limit load attribute index corresponding to the machine position YA19 and the limit load attribute indexes corresponding to the other machine positions in fig. 2 is obtained, so that the following values can be obtained in sequence: 0.3%, 3.3%, 4.2%, 4.5%, 4.7%, 4.8%, 4.9%, 5.1%, 5.4%. If the preset numerical intervals are [0, 10% ] and [ 10%, 20% ], the absolute values of the percentages of the above differences are within the numerical interval of [0, 10% ], and therefore, all the machine sites can be classified into the fan sets corresponding to the numerical interval of [0, 10% ], in other words, the result of grouping the limit load attribute indexes can be the same as that of fig. 2.

For another example, in the case that the deviation includes a percentage of the difference, in the example shown in fig. 3, the maximum value of all the determined fatigue load attribute indexes is the fatigue load attribute index 1 corresponding to the machine position YB2, and the absolute value of the percentage of the difference between the fatigue load attribute index corresponding to the machine position YB2 and the fatigue load attribute indexes corresponding to the other machine positions in fig. 3 is obtained, so that the following values can be obtained in sequence: 0.4%, 2.6%, 2.9%, 6.7%, 7.1%, 7.9%, 10.2%, 10.7%, 10.9%, 11.4%, 13.1%, 14.5%. If the preset value intervals are [0, 10% ] and [ 10%, 20% ], dividing the machine sites in the value interval with the absolute value of the percentage of the difference value of [0, 10% ] into a fan set, wherein the fan set comprises machine sites YB2, YA11, YA19, YA10, YA18, YA17, YA13 and YA16, and in the context of the present invention, the fan set obtained after grouping can be referred to as a first fan set; the machine positions within the numerical range of [ 10%, 20% ] of the absolute values of the percentages of the differences are divided into a fan set, and the fan set includes machine positions YA8, YA7, YA9, YA14, YA15, YA1 and YA 2. FIG. 4 illustrates a schematic diagram of a fatigue load attribute indicator for a wind parameter for a site included in the first set of wind turbines; FIG. 5 illustrates a schematic of fatigue load attribute indicators for wind parameters for various sites included in the second set of wind turbines.

After acquiring the plurality of fan sets, in step S500, the following operations are performed for any fan set in the plurality of fan sets: determining the envelope wind parameters of any fan set, and determining the representative wind parameters of any fan set based on the envelope wind parameters of any fan set.

By way of example, the envelope wind parameter for any set of fans refers to the maximum of the same type of wind parameters for the machine sites included in any set of fans. For example, assuming that the fan set a includes machine sites YA8, YA7 and YA9, wherein the wind parameter of the machine site YA8 is 7.14m/s of wind speed, the turbulence intensity is 9m/s, the wind parameter of the machine site YA7 is 7.34m/s of wind speed, the turbulence intensity is 7m/s, the wind parameter of the machine site YA9 is 7.53m/s of wind speed, and the turbulence intensity is 5m/s, the maximum value of the wind parameters of the three machine sites is selected, that is, the wind speed is 7.53m/s and the turbulence intensity is 9m/s, and the wind speed is 7.53m/s and the turbulence intensity is 9m/s can be taken as the envelope wind parameter of the fan set a.

In one example, determining a representative wind parameter for any set of fans based on the envelope wind parameter for any set of fans may be accomplished by:

firstly, inputting the enveloping wind parameters of any fan set into a load prediction model corresponding to a preset machine position point, and obtaining the enveloping key load of the preset machine position point. Here, the preset machine location may be a certain machine location in any fan set, or may be a virtual machine location virtualized by a computer program.

And then, determining the ratio of the envelope key load to the envelope key load as an envelope load attribute index corresponding to the envelope wind parameter of any fan set. Because the envelope wind parameter of any fan set is the maximum value in the wind parameters of all the organic sites in any fan set, the envelope key load is the maximum value in the key load of all the organic sites in any fan set, and therefore the envelope wind parameter can be compared with the envelope wind parameter of any fan set, and the attribute index of the envelope load is 1.

And then, respectively calculating the ratio of the key load of each machine position point in any fan set to the envelope key load, and determining each calculated ratio as a new load attribute index of the wind parameter of each corresponding machine position point.

Preferably, after obtaining the new load attribute indexes of the wind parameters of each machine location, the envelope load attribute indexes and the new load attribute indexes of each machine location may be sorted according to the preset sorting rule, for example, sorted in a descending order.

For example, in the example of fig. 2, the sorted envelope load attribute indexes and the sorting result of the new load attribute indexes of each machine location may be as shown in fig. 6, where a machine location point Uload _ all represents a preset machine location point.

In addition, in the example of fig. 3, the sorted envelope load attribute indexes and the sorting result of the new load attribute indexes of the wind parameters of the machine sites included in the aforementioned first wind turbine set may be as shown in fig. 7, where the Fload _01 represents a preset machine site. The sorted envelope load attribute indexes and the new load attribute indexes of the wind parameters of the machine sites included in the second wind turbine set described above may be as shown in fig. 8, where flow _02 represents a preset machine site.

And finally, determining the representative wind parameter of any fan set according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine position point in any fan set and the load attribute index of the wind parameter of each machine position point in any fan set.

According to one example, a representative wind parameter for any set of wind turbines may be determined by:

and comparing the envelope load attribute index with the maximum value in the new load attribute indexes of the wind parameters of all the machine sites in any fan set. For example, it may be determined whether the absolute value of the deviation of the envelope load attribute indicator from the maximum value in the new load attribute indicators for the wind parameters for the machine sites in any of the set of wind turbines is not greater than a first threshold. Here, the deviation is defined as described above, and for example, the deviation may include any one of the following items: the difference and the percentage of the difference. For example, where the deviation is a percentage of the difference, the first threshold may be a predetermined percentage threshold, e.g., 10%.

For example, referring to the example of fig. 6, the envelope load attribute index 1 of the preset machine location Uload _ all may be compared with the maximum value of 0.989 of the new load attribute indexes of the wind parameters of the machine locations in any of the fan sets. For example, it is determined whether the absolute value 1.1% of the deviation between the envelope load property index 1 and the maximum value 0.989 is not more than the first threshold 10%.

And if the absolute value of the deviation between the envelope load attribute index and the maximum value is not larger than a first threshold value, determining the envelope wind parameter corresponding to the envelope load attribute index as a representative wind parameter of any fan set.

For example, referring to fig. 2 and 6, assuming that the first threshold is 10%, and the absolute value 1.1% of the percentage of the difference between the envelope load attribute index 1 of the preset machine location Uload _ all in fig. 6 and the maximum value 0.989 in the new load attribute index of the wind parameter of each other machine location in fig. 6 is not greater than 10%, the envelope wind parameter corresponding to the envelope load attribute index 1 may be used to determine the representative wind parameter of the wind turbine set whose grouping result for the limit load attribute index is the same as that shown in the diagram of fig. 2.

And if the absolute value of the deviation between the envelope load attribute index and the maximum value is larger than a first threshold value, comparing the maximum value with the minimum value in the load attribute indexes of the wind parameters of all the machine sites in any fan set. For example, it is determined whether the absolute value of the deviation between the envelope load property indicator and the maximum value is not larger than a second threshold value, where the second threshold value is smaller than the first threshold value. Here, the second threshold may be a predetermined percentage threshold.

And if the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is larger than a second threshold value, continuously grouping the machine positions in any fan set until the envelope wind parameters of one group can be used as the representative wind parameters of the group. Here, the grouping manner of the continuation is the same as the grouping manner described above in the present invention, and the embodiment of the present invention is not described herein again.

For example, referring to fig. 5 and 8, assuming that the first threshold is 10%, the second threshold is 5%, and the absolute value of the percentage of difference between the envelope load attribute index 1 of the preset machine location point Fload _02 in fig. 8 and the maximum value 0.855 in the new load attribute index of the wind parameter of each other machine location point in fig. 8 is greater than 10%, it is determined whether the absolute value of the percentage of difference between the maximum value 0.898 and the minimum value 0.855 in the fatigue load attribute index of the wind parameter of each machine location point in the set of fans indicated in fig. 5 is not greater than the second threshold 5%, and it can be seen that the absolute value of the percentage of difference between the maximum value 0.898 and the minimum value 0.855 in the fatigue load attribute index of the wind parameter of each machine location point in the set indicated in fig. 5 is not greater than 4.3% of the second threshold 5%, the set of fans indicated in fig. 5 continues to be grouped as described above in the present invention, a set of envelope wind parameters obtained up to the grouping can be used as a representative wind parameter for the group.

Furthermore, additionally, if the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is not greater than the second threshold value, an individual analysis is performed for each machine location point, because the fact that the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is not greater than the second threshold value means that the magnitude of the critical load of each machine location point is equivalent, but the load variables most affected by the wind parameters are different from each other, and thus an individual detailed analysis is required from machine location point to machine location point. The invention is not described in detail herein, particularly with respect to the individual assay methods.

According to the method for determining the representative wind parameter of the wind farm, the problems that influence on the wind parameter is not considered comprehensively and the relation between the wind parameter and the load is not utilized sufficiently in the method for determining the representative wind parameter in the prior art can be solved.

Based on the same inventive concept as the method for determining the representative wind parameter of the wind farm shown in fig. 1, the embodiment of the invention also provides a device for determining the representative wind parameter of the wind farm, as described in the following embodiment. Since the principle of the device for solving the problem is similar to the method shown in fig. 1, the implementation of the device can refer to the implementation of the method for determining the representative wind parameter of the wind farm shown in fig. 1, and repeated details are omitted.

Fig. 9 shows a block diagram of an apparatus for determining a representative wind parameter of a wind farm according to an exemplary embodiment of the present invention. The wind farm comprises a plurality of machine sites for arranging wind generating sets.

As shown in fig. 9, the apparatus for determining a representative wind parameter of a wind farm according to an exemplary embodiment of the present invention mainly comprises: the wind parameter acquiring unit 100, the key load acquiring unit 200, the load attribute index determining unit 300, the fan set determining unit 400, and the representative wind parameter determining unit 500.

The wind parameter acquisition unit 100 acquires wind parameters of a plurality of machine sites. As an example, the wind parameters may include, but are not limited to, at least one of: turbulence intensity, air density, inflow angle, wind shear, wind frequency distribution value.

The key load obtaining unit 200 respectively inputs the obtained wind parameters of the plurality of machine positions to the corresponding load prediction models, so as to obtain the key loads of the plurality of machine positions. Here, the critical load of any machine site refers to the load variable of the wind generating set arranged at any machine site, which is influenced most by the wind parameters.

In one example, the critical load obtaining unit 200 inputs the wind parameters of any one of the machine sites to a load prediction model corresponding to a model of a wind turbine generator set disposed at any one of the machine sites to obtain the critical load of any one of the machine sites.

Specifically, the load prediction model corresponding to the wind turbine generator set of any model can be constructed by a model construction unit (not shown in fig. 9) additionally included in the equipment:

first, the model construction unit first obtains a plurality of sets of sample wind parameters.

And then, the model construction unit respectively determines the key loads of the wind generating set of any model under each group of sample wind parameters, and a load prediction model corresponding to the wind generating set of any model is constructed by using the multiple groups of sample wind parameters and the determined key loads.

In one example, the plurality of sets of sample wind parameters may include sample wind parameters under a plurality of sub-operating condition sets, and the critical load may include a limit load variable of the limit load that is most affected by the sample wind parameters. In this case, the model building unit respectively determines the limit load of the wind generating set of any model under the sample wind parameter of each sub-operating condition group through a simulation program, and determines the maximum value of all the determined limit loads as the limit load variable of the wind generating set of any model, wherein the load prediction model may include a model for determining the limit load variable of the wind generating set of any model.

In another example, the critical load may include a fatigue load variable most affected by the sample wind parameter among the fatigue loads, in which case, the model building unit determines the fatigue loads of the wind generating sets of any model under the sample wind parameter of each sub-condition through a simulation program, performs weighting processing on all the determined fatigue loads, obtains a load weighted value, and determines the obtained load weighted value as the fatigue load variable of the wind generating sets of any model, wherein the load prediction model includes a model for determining the fatigue load variable of the wind generating sets of any model.

Correspondingly, after the model construction unit determines each key load, a load prediction model corresponding to the wind generating set of any model can be obtained by performing multiple linear regression processing on the multiple groups of sample wind parameters and each determined key load. In addition, the model construction unit can also train the load prediction model by taking the multiple groups of sample wind parameters and the determined key loads as training samples, so that the load prediction model corresponding to the wind generating set of any model is obtained. In addition, the load prediction model corresponding to the wind turbine generator set of any model can also be obtained by using multiple sets of sample wind parameters and the determined critical loads in other existing manners, which is not limited by the invention.

On the other hand, in the example of the present invention, the critical load of any one machine site may refer to the load variable at which the wind turbine generator set arranged at any one machine site is most affected by the above-mentioned sample wind parameter. In this case, the model construction unit may determine the critical load of any one of the machine sites (i.e., the load variable of a certain model of wind turbine generator set arranged at any one of the machine sites that is most affected by the sample wind parameter) by: and determining a plurality of load variables of the wind generating set for testing, which is the same as the wind generating set arranged at any machine site, under each set of sample wind parameters in the plurality of sets of sample wind parameters, and determining the load variable with the largest change amplitude in the determined plurality of load variables as the key load of any machine site (namely, the load variable of the wind generating set arranged at any machine site, which is influenced most by the sample wind parameters).

The load attribute index determination unit 300 determines load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions.

In one example, the load attribute index determining unit 300 may respectively calculate a ratio of the key load of each of the plurality of machine locations to a maximum value of the key loads of the plurality of machine locations, and determine each calculated ratio as the load attribute index of the wind parameter of the corresponding machine location.

The fan set determination unit 400 groups a plurality of machine locations based on the determined load attribute index, obtaining a plurality of fan sets.

As an example, the fan set determination unit 400 determines an absolute value of a deviation of each load attribute index from a maximum value among all the determined load attribute indexes, and determines a numerical range to which the absolute value of each deviation belongs, one numerical range corresponding to one fan set, to obtain a plurality of fan sets.

In an example of the present invention, the deviation may include any one of: the difference and the percentage of the difference. Further, the deviation may be other physical quantities set according to actual conditions, and the present invention is not limited at all.

The representative wind parameter determination unit 500 performs the following for any one of the plurality of fan sets: determining the envelope wind parameters of any fan set, and determining the representative wind parameters of any fan set based on the envelope wind parameters of any fan set.

In one example, the representative wind parameter determination unit 500 includes an envelope critical load acquisition unit, an envelope load attribute index determination unit, a new load attribute index determination unit, and a fan set representative wind parameter determination unit (not shown in fig. 9).

The envelope key load obtaining unit inputs the envelope wind parameters of any fan set into a load prediction model corresponding to the preset machine position point, and the envelope key load of the preset machine position point is obtained. Here, the preset machine location may be a certain machine location in any fan set, or may be a virtual machine location virtualized by a computer program.

And the envelope load attribute index determining unit determines the ratio of the envelope key load to the envelope key load as an envelope load attribute index corresponding to the envelope wind parameter of any fan set. Because the envelope wind parameter of any fan set is the maximum value in the wind parameters of all the organic sites in any fan set, the envelope key load is the maximum value in the key load of all the organic sites in any fan set, and therefore the envelope wind parameter can be compared with the envelope wind parameter of any fan set, and the attribute index of the envelope load is 1.

And the new load attribute index determining unit respectively calculates the ratio of the key load of each machine position point in any fan set to the envelope key load, and determines each calculated ratio as the new load attribute index of the wind parameter of each corresponding machine position point.

The fan set representative wind parameter determination unit determines a representative wind parameter of any fan set according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine point in any fan set and the load attribute index of the wind parameter of each machine point in any fan set.

According to an example, the wind turbine set representative wind parameter determination unit 400 may determine the representative wind parameter of any of the wind turbine sets by:

and comparing the envelope load attribute index with the maximum value in the new load attribute indexes of the wind parameters of all the machine sites in any fan set. For example, it may be determined whether the absolute value of the deviation of the envelope load attribute indicator from the maximum value in the new load attribute indicators for the wind parameters for the machine sites in any of the set of wind turbines is not greater than a first threshold. Here, the deviation is defined as described above, and for example, the deviation may include any one of the following items: the difference and the percentage of the difference.

And if the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is larger than a second threshold value, continuously grouping the machine positions in any fan set until the envelope wind parameters of one group can be used as the representative wind parameters of the group.

And if the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is larger than a second threshold value, continuously grouping the machine positions in any one fan set until a group of envelope wind parameters can be used as the representative wind parameters of the group, wherein the second threshold value is smaller than the first threshold value.

It should be appreciated that a specific implementation of the apparatus for determining a representative wind parameter of a wind farm according to an exemplary embodiment of the present invention may be implemented with reference to the related specific implementation described in conjunction with fig. 1 to 8, and will not be described herein again.

Furthermore, it should be understood that the various units in the device for determining representative wind parameters of a wind farm according to an exemplary embodiment of the present invention may be implemented as hardware components and/or software components. The individual units may be implemented, for example, using Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs), depending on the processing performed by the individual units as defined by the skilled person.

An electronic device according to another exemplary embodiment of the present invention includes: a processor (not shown) and a memory (not shown) and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, implements the method of determining a representative wind parameter of a wind farm as in the above exemplary embodiments.

A computer readable storage medium according to an exemplary embodiment of the present invention stores a computer program which, when executed by a processor, causes the processor to perform the method of determining a representative wind parameter of a wind farm of the above-described exemplary embodiments. The computer readable storage medium is any data storage device that can store data which can be read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).

By utilizing the method and the equipment for determining the representative wind parameter of the wind farm according to the exemplary embodiment of the invention, the problems that the influence capacity on the wind parameter is not considered fully, the relation between the wind parameter and the load is not utilized sufficiently and the like in the method for determining the representative wind parameter in the prior art can be solved, and in addition, the machine sites are grouped according to a specific mode (such as numerical value interval division, sequencing and the like), so that the grouping of the machine sites and the selection of the enveloping wind parameter are more precise and accurate, the accuracy of the representative wind parameter of the wind farm is ensured, and the economic benefit of the design of the wind farm is effectively improved.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.

Claims

1. A method of determining a representative wind parameter of a wind park comprising a plurality of machine sites for arranging wind generator sets, characterized in that the method comprises:

acquiring wind parameters of the plurality of machine positions;

respectively inputting the acquired wind parameters of the plurality of machine positions into corresponding load prediction models to obtain the key loads of the plurality of machine positions;

determining load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions;

grouping the plurality of machine sites based on the determined load attribute indexes to obtain a plurality of fan sets;

performing the following for any of the plurality of fan sets: determining an envelope wind parameter of any fan set, and determining a representative wind parameter of any fan set based on the envelope wind parameter of any fan set.

2. The method of claim 1, wherein determining the load attribute indicator for the wind parameter for the plurality of loci based on the obtained key loads for the plurality of loci comprises:

and respectively calculating the ratio of the key load of each machine position point in the plurality of machine position points to the maximum value of the key loads of the plurality of machine position points, and determining each calculated ratio as the load attribute index of the wind parameter of each corresponding machine position point.

3. The method of claim 1, wherein grouping the plurality of sites based on the determined load attribute indicator, the obtaining a plurality of fan sets comprising:

respectively determining the absolute value of the deviation of each load attribute index from the maximum value of all the determined load attribute indexes;

and determining a numerical range to which the absolute value of each deviation belongs, wherein one numerical range corresponds to one fan set so as to obtain the plurality of fan sets.

4. The method of claim 1, wherein determining the representative wind parameter for the any set of fans based on the envelope wind parameter for the any set of fans comprises:

inputting the enveloping wind parameters of any fan set into a load prediction model corresponding to a preset machine position point to obtain an enveloping key load of the preset machine position point;

determining the ratio of the envelope key load to the envelope key load as an envelope load attribute index corresponding to an envelope wind parameter of any fan set;

respectively calculating the ratio of the key load of each machine position point in any fan set to the envelope key load, and determining each calculated ratio as a new load attribute index of the wind parameter of each corresponding machine position point;

and determining the representative wind parameter of any fan set according to the envelope load attribute index, the new load attribute index of the wind parameter of each machine position point in any fan set and the load attribute index of the wind parameter of each machine position point in any fan set.

5. The method of claim 4, wherein determining the representative wind parameter for the any one of the fan sets based on the envelope load attribute indicator, the new load attribute indicator for the wind parameter for each of the machine sites in the any one of the fan sets, and the load attribute indicator for the wind parameter for each of the machine sites in the any one of the fan sets comprises:

comparing the envelope load attribute index with the maximum value of the new load attribute indexes of the wind parameters of the machine sites in any fan set,

if the absolute value of the deviation between the envelope load attribute index and the maximum value is not larger than a first threshold value, determining the envelope wind parameter corresponding to the envelope load attribute index as a representative wind parameter of any one fan set,

if the absolute value of the deviation between the envelope load attribute index and the maximum value is larger than a first threshold value, comparing the maximum value with the minimum value in the load attribute indexes of the wind parameters of all the machine sites in any one fan set,

if the absolute value of the deviation between the maximum value and the minimum value in the load attribute index is larger than a second threshold value, continuously grouping the machine positions in any one fan set until the envelope wind parameters of one group can be used as the representative wind parameters of the group,

wherein the second threshold is less than the first threshold.

6. The method of claim 1, wherein the critical load for any of each of the loci is obtained by:

inputting the wind parameters of the any one machine site into a load prediction model corresponding to the model of the wind generating set arranged at the any one machine site to obtain the key load of the any one machine site,

the method comprises the following steps of constructing a load prediction model corresponding to a wind generating set of any model in the following mode:

acquiring a plurality of groups of sample wind parameters, wherein the plurality of groups of sample wind parameters are acquired based on a preset sample design method;

respectively determining the key load of the wind generating set of any model under each group of sample wind parameters;

and constructing a load prediction model corresponding to the wind generating set of any model by using the multiple groups of sample wind parameters and the determined key loads.

7. The method of claim 6, wherein the plurality of sets of sample wind parameters includes sample wind parameters under a plurality of sub-sets of conditions, the critical load includes a variable of a limit load that is most affected by the sample wind parameters among the limit loads,

the method comprises the following steps of determining the key load of the wind generating set of any model under any group of sample wind parameters:

respectively determining the limit load of the wind generating set of any model under the sample wind parameter of each sub-working condition group through a simulation program,

determining the maximum value of all the determined limit loads as the limit load variable of the wind generating set of any model,

wherein the load prediction model comprises a model for determining extreme load variables of the wind power plant of any model.

8. The method of claim 6, wherein the plurality of sets of sample wind parameters includes sample wind parameters under a plurality of sub-conditions, the critical loads include fatigue load variables of the fatigue loads most affected by the sample wind parameters,

respectively determining fatigue loads of the wind generating sets of any type under the sample wind parameters under each sub-working condition through a simulation program,

weighting all the determined fatigue loads to obtain a load weighted value, determining the obtained load weighted value as a fatigue load variable of the wind generating set of any machine type,

wherein the load prediction model comprises a model for determining fatigue load variables of the wind turbine generator set of any model.

9. The method of claim 6, wherein the step of constructing a load prediction model for a wind generating set of any model using the plurality of sets of sample wind parameters and the determined critical loads comprises:

and performing multiple linear regression processing on the multiple groups of sample wind parameters and the determined key loads to obtain a load prediction model corresponding to the wind generating set of any model.

10. The method of claim 6, wherein the critical load at any one of the sites is a load variable at which a wind turbine generator set disposed at the any one of the sites is most affected by the sample wind parameter.

11. The method of claim 10, wherein the critical load for any of the machine sites is determined by:

determining a plurality of load variables of a wind generating set for testing under each of the plurality of sets of sample wind parameters, the wind generating set being of the same model as a wind generating set disposed at the any one of the machine sites;

and determining the load variable with the largest change amplitude in the plurality of determined load variables as the key load of any machine point.

12. A device for determining a representative wind parameter of a wind park comprising a plurality of machine sites for arranging wind generator sets, characterized in that it comprises:

the wind parameter acquisition unit is used for acquiring wind parameters of the plurality of machine sites;

the key load acquisition unit is used for respectively inputting the acquired wind parameters of the plurality of machine points into corresponding load prediction models to obtain the key loads of the plurality of machine points;

a load attribute index determination unit which determines load attribute indexes of the plurality of machine positions based on the obtained key loads of the plurality of machine positions;

the fan set determining unit is used for grouping the plurality of machine positions based on the determined load attribute indexes to obtain a plurality of fan sets;

a representative wind parameter determination unit that performs, for any one of the plurality of fan sets: determining an envelope wind parameter of any fan set, and determining a representative wind parameter of any fan set based on the envelope wind parameter of any fan set.

13. An electronic device, characterized in that the electronic device comprises: a processor, a memory, and a computer program stored on the memory and executable on the processor;

the processor, when executing the computer program, implements a method of determining a representative wind parameter of a wind farm according to any of claims 1 to 11.

14. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method of determining a representative wind parameter of a wind farm according to any one of the claims 1 to 11.